import tensorflow as tf

def load_data_and_labels():
	return

timestep_size = max_length = 25
hidden_size = 100
batch_size = 10
embedding_size = 100
vocabulary = [1*1000]       # 词汇表长度

# features, labels = load_data_and_labels()

b_features = tf.placeholder(dtype=tf.int32, shape=[None, timestep_size], name="batch_features")
b_labels = tf.placeholder(dtype=tf.int32, shape=[None], name="batch_labels")


lstm = tf.nn.rnn_cell.GRUCell(num_units=hidden_size)        # 构建LSTM层
initial_state_lstm = lstm.zero_state(batch_size, dtype=tf.float32)        # 初始状态全为0
# 一直有个疑问, 返回值里的states是干啥的, 为啥一直不用
outputs, states = tf.nn.dynamic_rnn(lstm, b_features, initial_state=initial_state_lstm)

print(outputs)

with tf.Session() as sess:
	sess.run()

